1. Field of the Invention
The invention relates generally to oil and gas exploration, particularly to methods and systems for deriving formation properties from formation logging data, more particularly to the decomposition of complex data sets for use in interpreting well logging data.
2. Background Art
Subsurface or downhole logging techniques are realized in different ways as known in the art. A well tool, comprising a number of transmitting and detecting devices for measuring various parameters, can be lowered into a borehole on the end of a cable or wireline. The cable, which is attached to some mobile processing center at the surface, is the means by which parameter data may be sent up to the surface. With this type of logging, it becomes possible to measure borehole and formation parameters as a function of depth, i.e., while the tool is being pulled uphole.
An alternative to wireline logging techniques is the collection of data on downhole conditions during the drilling process. By collecting and processing such information during the drilling process, the driller can modify or correct key steps in the operation to optimize performance. Schemes for collecting data of downhole conditions and movement of the drilling assembly during the drilling operation are known as measurement-while-drilling (MWD) techniques. Similar techniques focusing more on measurement of formation parameters than on movement of the drilling assembly are known as logging-while-drilling (LWD). Note that drilling operations may also use casings or coil tubings instead of conventional drill strings. Casing drilling and coil tubing drilling are well known in the art. In these situations, logging operations may be similarly performed as in conventional MWD or LWD. In this description, “logging-while-drilling” will be generally used to include the use of a drill string, a casing, or a coil tubing, and hence MWD and LWD are intended to include operations using casings or coil tubings. Logging-while-tripping (LWT) is an alternative to LWD and MWD techniques. In LWT, a small diameter “run-in” tool is sent downhole through the drill pipe, at the end of a bit run, just before the drill pipe is pulled. The run-in tool is used to measure the downhole physical quantities as the drill string is extracted or tripped out of the hole. Measured data is recorded into tool memory versus time during the trip out. At the surface, a second set of equipment records bit depth versus time for the trip out, and this allows the measurements to be placed on depth. Sensors or tools permanently placed in a wellbore may also be used to obtain log data. Embodiments of the invention may use data obtained with any of these different logging methods.
FIG. 1 shows a typical LWD system that includes a derrick 10 positioned over a borehole 11. A drilling tool assembly, which includes a drill string 12 and drill bit 15, is disposed in the borehole 11. The drill string 12 and bit 15 are turned by rotation of a Kelly 17 coupled to the upper end of the drill string 12. The Kelly 17 is rotated by engagement with a rotary table 16 or the like forming part of the rig 10. The Kelly 17 and drill string 12 are suspended by a hook 18 coupled to the Kelly 17 by a rotatable swivel 19. Drilling fluid (mud) 6 is stored in a pit 7 and is pumped through the center of the drill string 12 by a mud pump 9 to flow downwardly. After circulation through the bit 15, the drilling fluid circulates upwardly through an annular space between the borehole 11 and the outside of the drill string 12. Flow of the drilling mud 6 lubricates and cools the bit 15 and lifts drill cuttings made by the bit 15 to the surface for collection and disposal. As shown, a logging tool 14 is connected to the drill string 12. Signals measured by the logging tool 14 may be transmitted to the surface computer system 13 or stored in memory (not shown) onboard the tool 14. The logging tool 14 may include any number of conventional sources and/or sensors known in the art.
Formation logging data obtained in wellbore need to be transmitted to surface for analysis. However, these data are often quite voluminous and are difficult to transmit efficiently, especially from LWD, MWD, or LWT operations. In addition, these data need to be processed to derive formation properties (formation profiles). The large volume of data do not lend themselves to easy transmission or analysis. One approach to overcome this problem is to preprocess the large amount of data into a smaller subset that still represents the original data, for example by decomposing the complex distribution into individual components with well defined parameters. One particularly attractive approach is to decompose the complex data into individual components that corresponds to the underlying physical events.
However, many petrophysical parameters (e.g. porosity, fracture spacing) have complex distributions that are often the result of several natural phenomena or physical processes superimposing themselves on each other (e.g., grain interpososity, vug porosity, multiple fracture sets from different geological events). Log data therefore often manifest themselves as complex distributions of overlapping components. As a result, the discrete phenomena, processes, etc. that contribute to such complex distributions of log data are difficult to extract.
U.S. Pat. No. 7,133,777 issued to the Goswami et al. discloses methods for decomposing complex distributions into a set of underlying simpler components that can be individually processed. These methods decompose complex distributions of data by modeling the complex distribution as a sum of discrete simple distributions (such as Gaussian distributions) and extract parameters of these discrete simple distributions to facilitate data transmission and analysis.
The methods disclosed in the '777 patent demonstrated the usefulness of such an approach. However, there is still a need for methods that can accurately simplify complex distributions of data so that they can be easily transmitted and used to reconstruct the events that underlie the complex distributions of the measurement data.